English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Although proper handling of discourse significantly contributes to thequality of machine translation (MT), these improvements are not adequatelymeasured in common translation quality metrics. Recent works in context-awareMT attempt to target a small set of discourse phenomena during evaluation,however not in a fully systematic way. In this paper, we develop theMultilingual Discourse-Aware (MuDA) benchmark, a series of taggers thatidentify and evaluate model performance on discourse phenomena in any givendataset. The choice of phenomena is inspired by a novel methodology tosystematically identify translations requiring context. We confirm thedifficulty of previously studied phenomena while uncovering others that werepreviously unaddressed. We find that common context-aware MT models make onlymarginal improvements over context-agnostic models, which suggests these modelsdo not handle these ambiguities effectively. We release code and data for 14language pairs to encourage the MT community to focus on accurately capturingdiscourse phenomena.

When Does Translation Require Context? A Data-driven, Multilingual Exploration